The AI Engineer (Remote) is responsible for designing, developing, deploying, and maintaining enterprise-grade AI and machine learning (ML) solutions that integrate seamlessly across the company's technology ecosystem - including Microsoft Copilot Studio, Power Platform, and AWS-based ML environments.
This hybrid role bridges AI/ML development, agentic AI orchestration, and MLOps infrastructure, ensuring that models and intelligent agents are secure, scalable, and business-aligned.
The position advances the company's use of AI across all internal and external customer experiences by:
- Building and operationalizing AI models and copilots that augment business workflows.
- Integrating models into Microsoft Copilot Studio, Power Apps, and Power Automate.
- Managing the lifecycle of models deployed through AWS SageMaker, Azure ML, or internal APIs.
Essential Job Duties/Responsibilities:
A. AI / ML & Agentic AI Engineering
- Design, fine-tune, and deploy AI/ML models, including LLMs and retrieval-augmented generation (RAG) pipelines.
- Develop and maintain custom Copilots and intelligent agents within Microsoft Copilot Studio that interact with enterprise data and APIs.
- Implement Power Platform integrations (Power Apps, Power Automate, Power Virtual Agents) for business-facing AI workflows.
- Collaborate with data scientists to transform prototypes into production-grade, secure, and compliant solutions.
- Build domain-specific AI skills and agents tailored to insurance and financial processes.
B. MLOps / Platform Engineering
- Develop and automate ML pipelines for training, deployment, and monitoring on AWS SageMaker and Azure ML.
- Manage CI/CD for AI models and Copilot plugins using MLflow, Airflow, Docker, and Kubernetes.
- Implement monitoring for Copilot-based and standalone AI agents, capturing performance, drift, and usage analytics.
- Integrate AI governance frameworks ensuring auditability, explainability, and data privacy compliance.
C. Collaboration and Support
- Partner with software engineering and Power Platform teams to embed AI services into core business systems.
- Collaborate with IT and Security on Copilot extensions and secure connectors for enterprise data.
- Mentor junior AI engineers on cloud AI tools, Copilot integration, and MLOps best practices.
Experience:
Required
- 2-5 years combined experience in software engineering, ML, and/or MLOps.
- Proven success deploying AI/ML models in Microsoft or AWS environments.
- Strong proficiency in Python, ML frameworks (Scikit-learn, PyTorch, TensorFlow), and API integration.
- Hands-on experience with Microsoft Copilot Studio, Power Automate, and Power Apps development.
- Experience deploying AI pipelines in AWS SageMaker or Azure ML.
- Familiarity with LLM-based copilots, agent orchestration, and prompt engineering.
Preferred
- Experience connecting Copilot agents to proprietary datasets via secure connectors or APIs.
- Knowledge of vector databases (Pinecone, FAISS) and RAG architectures.
- Familiarity with Azure OpenAI Service, Dataverse, and Microsoft Graph APIs.
- Background in financial or insurance domain AI use cases.
Education:
- Bachelor's or Master's degree in Computer Science, Data Science, or a related technical field.
Certifications (Preferred):
- Microsoft Certified: Azure AI Engineer Associate (AI-102)
- Microsoft Certified: Power Platform Solution Architect / Developer Associate
- AWS Certified Machine Learning - Specialty
- Microsoft Copilot Studio or AI Builder training credentials
Physical Requirements:
- Representative of those that must be met by an employee to successfully perform the essential function of the job. Must be able to operate a PC, other relevant office tools/equipment, sit for extended periods, and/or occasionally stand for extended periods, lead training classes and travel. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.
Compensation:
Compensation offered for this role is 90,000.00-175,000.00 annually and is based on experience and qualifications.